A SNR-MCS dynamic mapping table design method

Abstract

The application discloses a SNR-MCS dynamic mapping table design method, and belongs to the technical field of wireless communication, comprising: continuously updating the SNR-MCS dynamic mapping table; outputting the transmission success probability based on the probability prediction model according to the signal-to-noise ratio and the modulation and coding mode; screening the MCS candidate set according to the transmission success probability to construct the MCS prediction model; collecting the actual signal-to-noise ratio to input the MCS prediction model to output the modulation and coding mode, and collecting the transmission feedback to update the data pool; dividing the signal-to-noise ratio into several segments, generating the SNR-MCS dynamic mapping table based on the coverage range of the signal-to-noise ratio of each segment and the MCS prediction model; using the SNR-MCS dynamic mapping table for interaction and updating the data pool; and retraining the probability prediction model through the updated data pool. The application solves the problem of low transmission efficiency and reliability caused by the fact that the prior art cannot adapt to the changing channel conditions.

Description

Technical Field

[0001] This invention relates to a dynamic mapping table design method for SNR-MCS, belonging to the field of wireless communication technology. Background Technology

[0002] Adaptive Modulation and Coding (AMC), a typical link adaptation technique, has been widely used in wireless communication since its inception in 1968. Studies have shown that in time-varying fading channels and multipath fading channels, dynamically adjusting the modulation and coding scheme can significantly improve system throughput. With the continuous development of communication technology, AMC has been incorporated into Long Term Evolution (LTE) systems to match channel characteristics and maximize effective communication rates. In 5G communication systems, AMC is further applied, aiming to dynamically match transmission parameters with changing wireless channel conditions to improve bandwidth utilization and throughput performance. Currently, research on AMC mainly focuses on AMC based on the classic Outer Loop Link Adaptation (OLLA) algorithm, lookup table methods based on SNR-MCS mapping, and AMC based on intelligent methods.

[0003] However, existing technologies still have many limitations. First, while OLLA-based AMC methods optimize MCS selection by adjusting the SNR threshold, their performance is highly dependent on hyperparameter settings, and their convergence speed is limited by the feedback frequency of the Hybrid Automatic Repeat Request (HARQ) process, making them difficult to adapt to highly dynamic channel conditions. Second, while SNR-MCS mapping-based lookup table methods perform well in scenarios with relatively stable channel characteristics, constructing a universal mapping table is challenging due to the diversity and time-varying nature of channel conditions, and the high sensitivity of MCS selection to SNR limits their applicability. Finally, while intelligent methods such as Q-learning or deep learning-based AMC techniques offer better adaptability, their implementation complexity is high, training time is long, and their dependence on large amounts of training data limits their application in practical systems.

[0004] With the development of 5G and future 6G communication technologies, the communication environment will become more complex, and the time-varying and uncertain nature of channels will further increase. Existing AMC (Adaptive Computational Model) technologies still have shortcomings in terms of dynamic adaptability, generalization ability, and computational efficiency, making it difficult to meet the high efficiency and reliability requirements of future communication systems. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic SNR-MCS mapping table design method. By utilizing the advantages of neural networks in handling nonlinear problems, the mapping relationship between SNR and MCS is designed to solve the problem of low transmission efficiency and reliability caused by the inability of existing technologies to adapt to constantly changing channel conditions.

[0006] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0007] This invention provides a method for designing an SNR-MCS dynamic mapping table, comprising:

[0008] The system acquires communication data between the user and the base station and constructs a data pool, wherein the communication data includes signal-to-noise ratio, modulation and coding scheme, and transmission feedback.

[0009] Train a probabilistic prediction model using a data pool;

[0010] Repeat the following steps to continuously update the SNR-MCS dynamic mapping table:

[0011] Based on the signal-to-noise ratio and modulation and coding scheme, a real-time prediction is made using a trained probability prediction model, and the probability of successful transmission of the communication data is output.

[0012] Establish an MCS candidate set based on the modulation and coding schemes in the data pool;

[0013] Based on the success probability of the communication data transmission, a candidate set of MCS models is selected to construct an MCS prediction model;

[0014] The signal-to-noise ratio in the actual operating environment is collected and input into the MCS prediction model, and the corresponding modulation and coding scheme is output.

[0015] The user and base station interact using the corresponding modulation and coding scheme, and the transmission feedback is collected.

[0016] The data pool is updated based on the signal-to-noise ratio, the corresponding modulation and coding scheme, and the transmission feedback.

[0017] The signal-to-noise ratio (SNR) is divided into several segments based on the coverage and resolution of the SNR in the data pool. Based on the coverage of the SNR in each segment and the MCS prediction model, an SNR-MCS dynamic mapping table is generated.

[0018] The SNR-MCS dynamic mapping table is used for interaction between users and base stations, and the signal-to-noise ratio, modulation and coding scheme, and transmission feedback are collected to update the data pool.

[0019] The probabilistic prediction model is retrained using the updated data pool.

[0020] Furthermore, the probability prediction model includes a two-layer hidden binary classification neural network and a Hard-Sigmoig function. After the signal-to-noise ratio and modulation coding scheme are processed by the binary classification neural network, the data is input into the Hard-Sigmoig function for further processing, and the transmission success probability is output.

[0021] Furthermore, the probability of successful transmission is expressed as:

[0022] ;

[0023] In the formula, This represents the signal-to-noise ratio based on the interaction between the user and the base station. and modulation coding method The obtained transmission success probability, where, This indicates that the transmission was successful. This represents the Hard-Sigmoig function. This represents the signal-to-noise ratio (SNR) of the interaction between the user and the base station received by the binary classification neural network. and modulation coding method The output of .

[0024] Furthermore, the loss function for training the probabilistic prediction model is expressed as:

[0025] ;

[0026] In the formula, This represents the loss value of the weights in a binary classification neural network. This represents the number of training data samples in the data pool. This represents the first training data in the data pool. One sample, Indicates the first One sample was successfully transmitted. Represents the logarithmic function. This indicates the first interaction between the user and the base station. Signal-to-noise ratio of each sample and modulation coding method The obtained transmission success probability.

[0027] Furthermore, an MCS candidate set is established based on the modulation and coding schemes in the data pool, including:

[0028] The modulation order setting range of the modulation coding scheme is determined based on the minimum and maximum modulation order of the MCS in the data pool;

[0029] The bit rate setting range of the modulation and coding scheme is determined based on the minimum and maximum bit rates of the MCS in the data pool;

[0030] Multiple modulation order values ​​are selected within the modulation order setting range, and multiple code rate values ​​are selected within the code rate setting range. The selected modulation order values ​​and the selected code rate values ​​are cross-combined to generate candidate MCS entries.

[0031] Traverse the candidate MCS entries:

[0032] If the spectral efficiency of one candidate MCS entry is higher than that of another candidate MCS entry, and the modulation order is lower than that of another candidate MCS entry, then the other candidate MCS entry is eliminated.

[0033] After the traversal is complete, we obtain the candidate set of MCS.

[0034] Furthermore, the MCS candidate set includes several MCS entries, each MCS entry including an index identifier, modulation scheme or order, code rate, and spectral efficiency, wherein the index identifier and spectral efficiency have a monotonically increasing relationship.

[0035] Further, based on the success probability of the communication data transmission, a candidate set of MCSs is selected, and an MCS prediction model is constructed, including:

[0036] Traverse the MCS candidate set;

[0037] For each MCS entry, calculate the product of spectral efficiency and transmission success probability;

[0038] The MCS entry that maximizes the product of spectral efficiency and transmission success probability is selected as the MCS prediction model.

[0039] Furthermore, the MCS entry representing the maximum value of the product of spectral efficiency and transmission success probability is expressed as:

[0040] ;

[0041] In the formula, The MCS entry represents the maximum value of the product of spectral efficiency and transmission success probability, where... Indicates the signal-to-noise ratio. Indicates to make The MCS entry that yields the maximum value. This represents the signal-to-noise ratio based on the interaction between the user and the base station. and modulation coding method The obtained transmission success probability Modulation coding method Spectral efficiency.

[0042] Furthermore, the method for determining the signal-to-noise ratio coverage and resolution in the data pool includes:

[0043] The mean of the signal-to-noise ratio is calculated by statistically analyzing the signal-to-noise ratio in the data pool. and standard deviation ;

[0044] Set the lower limit of the signal-to-noise ratio coverage to The upper limit is set to ;

[0045] Set the signal-to-noise ratio resolution based on the coverage area.

[0046] Furthermore, the signal-to-noise ratio (SNR) is divided into several segments. Based on the SNR coverage of each segment and the MCS prediction model, an SNR-MCS dynamic mapping table is generated, including:

[0047] The signal-to-noise ratio (SNR) is divided into several segments based on its coverage and resolution.

[0048] Input the lower bound of the signal-to-noise ratio coverage of each segment into the MCS prediction model to obtain the corresponding MCS prediction value;

[0049] By constructing a mapping between the coverage area of ​​the signal-to-noise ratio and the corresponding MCS prediction value, an SNR-MCS dynamic mapping table is generated.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0051] 1. This invention can capture channel quality changes in real time and intelligently select the optimal MCS scheme based on a trained probability prediction model. This enables the target communication system to dynamically select the modulation and coding scheme with the highest matching degree with the current SNR while ensuring transmission reliability. Compared with the traditional static mapping table scheme, this invention can improve the spectral efficiency in scenarios with rapidly changing channel conditions. This invention also ensures that the target communication system always operates at the optimal balance point of spectral efficiency and transmission reliability through a continuously updated SNR-MCS dynamic mapping table. This invention solves the problem of low transmission efficiency and reliability caused by the inability of existing technologies to adapt to constantly changing channel conditions.

[0052] 2. This invention continuously updates the SNR-MCS mapping relationship through a closed-loop feedback mechanism, enabling the target communication system to automatically select the optimal modulation and coding scheme based on real-time channel conditions, maximizing spectrum utilization efficiency while ensuring transmission reliability. This invention breaks through the efficiency bottleneck of traditional MCS selection schemes by using a screening strategy that maximizes the product of spectrum efficiency and transmission success rate, achieving better spectrum resource allocation under the same bit error rate. Attached Figure Description

[0053] Figure 1This is a flowchart illustrating an SNR-MCS dynamic mapping table design method provided in an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the data pool update mechanism provided in an embodiment of the present invention;

[0055] Figure 3 This is a flowchart illustrating the application of the MCS prediction model provided in an embodiment of the present invention;

[0056] Figure 4 This is a simulation diagram comparing the spectral efficiency performance of the present invention and the outer loop link adaptive algorithm under different signal-to-noise ratios, provided by an embodiment of the present invention.

[0057] Figure 5 This is a simulation diagram comparing the bit error rate performance of the present invention and the outer loop link adaptive algorithm under different signal-to-noise ratios, provided by an embodiment of the present invention. Detailed Implementation

[0058] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0059] Example 1

[0060] like Figure 1 As shown in the figure, this embodiment introduces a method for designing an SNR-MCS dynamic mapping table, including:

[0061] Step 1: Acquire communication data between the user and the base station and construct a data pool. The communication data includes signal-to-noise ratio, modulation and coding scheme, and transmission feedback.

[0062] This invention constructs a data pool using communication data from various traditional fixed modulation and coding methods. The continuous updating of the data pool enables the target communication system to perceive signal-to-noise ratio and short-term dynamic changes.

[0063] Step 2: Train a probabilistic prediction model using a data pool.

[0064] This invention maps the signal-to-noise ratio and modulation and coding scheme combination in the data pool into a measurable success probability, providing a quantitative basis for MCS screening. The trained probability prediction model can quickly predict transmission performance based on any signal-to-noise ratio and modulation and coding scheme, adapting to real-time decision-making needs.

[0065] Step 3: Repeat the following steps to continuously update the SNR-MCS dynamic mapping table:

[0066] Based on the signal-to-noise ratio and modulation and coding scheme, a pre-trained probability prediction model is used to make real-time predictions and output the probability of successful transmission of the communication data.

[0067] This invention inputs the current signal-to-noise ratio and modulation and coding scheme into a trained probability prediction model, calculates the prediction success probability of each MCS, and evaluates the reliability of different MCSs under the current channel conditions in real time, thus avoiding the rigidity problem of fixed mapping tables.

[0068] A candidate set of MCSs is established based on the modulation and coding schemes in the data pool.

[0069] This invention combines the physical layer constraints of the target communication system to generate a set of MCS candidate sets that meet the conditions, ensuring that the establishment of the MCS candidate set complies with the system hardware capabilities and protocol specifications.

[0070] Based on the success probability of the communication data transmission, a candidate set of MCS models is selected to construct an MCS prediction model.

[0071] This invention traverses the candidate set of MCSs, calculates the product of the spectral efficiency and the prediction success probability of each MCS, and selects the MCS with the largest product as the optimal solution. Under the premise of ensuring the transmission success rate, it maximizes the spectral utilization rate and breaks through the limitation of traditional solutions where it is difficult to balance the two.

[0072] The signal-to-noise ratio in the actual operating environment is collected and input into the MCS prediction model, and the corresponding modulation and coding scheme is output.

[0073] This invention monitors the signal-to-noise ratio (SNR) of the current channel in real time, inputs the SNR into a pre-constructed MCS prediction model, outputs a recommended optimal MCS, and applies the results of the MCS prediction model to the actual communication link, thus completing a closed loop from theoretical optimization to engineering practice.

[0074] The user and base station interact using the corresponding modulation and coding scheme, and the transmission feedback is collected.

[0075] This invention verifies the effectiveness of the MCS prediction model through actual transmission results, providing real-time samples for subsequent data pool updates and probabilistic prediction model retraining.

[0076] The data pool is updated based on the signal-to-noise ratio, the corresponding modulation and coding scheme, and the transmission feedback.

[0077] This invention adds the newly acquired signal-to-noise ratio, the corresponding modulation and coding scheme, and the transmission feedback to the data pool, enabling continuous learning of dynamic channel changes and preventing the probability prediction model from failing due to environmental drift.

[0078] The signal-to-noise ratio (SNR) is divided into several segments based on the coverage and resolution of the SNR in the data pool. Based on the coverage of the SNR in each segment and the MCS prediction model, an SNR-MCS dynamic mapping table is generated.

[0079] This invention discretizes the continuous signal-to-noise ratio space into finite intervals, reducing the complexity of the mapping table. It automatically matches the actual channel quality distribution through a segmentation strategy, avoiding performance loss caused by fixed segmentation.

[0080] The SNR-MCS dynamic mapping table is used for interaction between users and base stations, and the signal-to-noise ratio, modulation and coding scheme, and transmission feedback are collected to update the data pool.

[0081] This invention transforms complex model prediction results into a mapping relationship that can be directly looked up in a table, reducing the complexity of real-time decision-making. At the same time, the SNR-MCS dynamic mapping table is dynamically adjusted as the data pool is updated, always reflecting the latest channel status.

[0082] The probabilistic prediction model is retrained using the updated data pool.

[0083] This invention retrains the probabilistic prediction model using an updated data pool, adjusts the neural network weights to adapt to channel changes, and avoids the probabilistic prediction model from becoming ineffective due to long-term channel changes or upgrades to the target communication system. Incremental training allows the probabilistic prediction model to gradually approach the real channel characteristics, reducing prediction errors.

[0084] Example 2

[0085] Based on the same inventive concept as Embodiment 1, this embodiment introduces the implementation steps of an SNR-MCS dynamic mapping table design method, including:

[0086] Step 1: Acquire communication data between the user and the base station and construct a data pool. The communication data includes signal-to-noise ratio, modulation and coding scheme, and transmission feedback.

[0087] Step 2: Train a probabilistic prediction model using a data pool.

[0088] In this embodiment, the probability prediction model includes a two-layer hidden binary classification neural network and a Hard-Sigmoig function. After the signal-to-noise ratio and modulation coding scheme are processed by the binary classification neural network, the data is input into the Hard-Sigmoig function for further processing, and the transmission success probability is output.

[0089] In this embodiment, the probability of successful transmission is expressed as:

[0090] ;

[0091] In the formula, This represents the signal-to-noise ratio based on the interaction between the user and the base station. and modulation coding method The obtained transmission success probability, where, This indicates that the transmission was successful. This represents the Hard-Sigmoig function. This represents the signal-to-noise ratio (SNR) of the interaction between the user and the base station received by the binary classification neural network. and modulation coding method The output of .

[0092] In this embodiment, the loss function for training the probabilistic prediction model is expressed as:

[0093] ;

[0094] In the formula, This represents the loss value of the weights in a binary classification neural network. This represents the number of training data samples in the data pool. This represents the first training data in the data pool. One sample, Indicates the first One sample was successfully transmitted. Represents the logarithmic function. This indicates the first interaction between the user and the base station. Signal-to-noise ratio of each sample and modulation coding method The obtained transmission success probability.

[0095] Step 3: Repeat the following steps to continuously update the SNR-MCS dynamic mapping table:

[0096] Step 3.1: Based on the signal-to-noise ratio and modulation and coding scheme, perform real-time prediction using the trained probability prediction model, and output the success probability of the communication data transmission.

[0097] Step 3.3: Establish an MCS candidate set based on the modulation and coding schemes in the data pool;

[0098] In this embodiment, establishing an MCS candidate set based on the modulation and coding scheme in the data pool includes:

[0099] The modulation order setting range of the modulation coding scheme is determined based on the minimum and maximum modulation order of the MCS in the data pool.

[0100] The bit rate setting range of the modulation and coding scheme is determined based on the minimum and maximum bit rates of the MCS in the data pool.

[0101] Multiple modulation order values ​​are selected within the modulation order setting range, and multiple code rate values ​​are selected within the code rate setting range. The selected modulation order values ​​and the selected code rate values ​​are cross-combined to generate candidate MCS entries.

[0102] Traverse the candidate MCS entries:

[0103] If the spectral efficiency of one candidate MCS entry is higher than that of another candidate MCS entry, and the modulation order is lower than that of the other candidate MCS entry, then the other candidate MCS entry is eliminated.

[0104] After the traversal is complete, we obtain the candidate set of MCS.

[0105] In this embodiment, the MCS candidate set includes several MCS entries. Each MCS entry includes an index identifier, modulation scheme or order, code rate, and spectral efficiency. The index identifier and spectral efficiency have a monotonically increasing relationship.

[0106] Step 3.3: Filter the MCS candidate set and construct the MCS prediction model based on the success probability of the communication data transmission.

[0107] In this embodiment, the flowchart of applying the MCS prediction model is shown below. Figure 3 As shown, the process of filtering the MCS candidate set based on the success probability of the communication data transmission and constructing the MCS prediction model includes:

[0108] Step 3.3.1: Traverse the MCS candidate set:

[0109] For each MCS entry, calculate the product of spectral efficiency and transmission success probability.

[0110] The MCS entry that maximizes the product of spectral efficiency and transmission success probability is selected as the MCS prediction model.

[0111] In this embodiment, the MCS entry representing the maximum value of the product of spectral efficiency and transmission success probability is expressed as:

[0112] ;

[0113] In the formula, The MCS entry represents the maximum value of the product of spectral efficiency and transmission success probability, where... Indicates the signal-to-noise ratio. Indicates to make The MCS entry that yields the maximum value. This represents the signal-to-noise ratio based on the interaction between the user and the base station. and modulation coding method The obtained transmission success probability Modulation coding method Spectral efficiency.

[0114] Step 3.4: Collect the signal-to-noise ratio in the actual operating environment and input it into the MCS prediction model, and output the corresponding modulation and coding scheme.

[0115] Step 3.5: Use the corresponding modulation and coding scheme to interact between the user and the base station and collect transmission feedback.

[0116] Step 3.6: Update the data pool based on the signal-to-noise ratio, the corresponding modulation and coding scheme, and the transmission feedback.

[0117] Step 3.7: Divide the signal-to-noise ratio into several segments according to the coverage and resolution of the signal-to-noise ratio in the data pool. Based on the coverage of the signal-to-noise ratio of each segment and the MCS prediction model, generate an SNR-MCS dynamic mapping table.

[0118] In this embodiment, the method for determining the signal-to-noise ratio coverage and resolution in the data pool includes:

[0119] The mean of the signal-to-noise ratio is calculated by statistically analyzing the signal-to-noise ratio in the data pool. and standard deviation ;

[0120] Set the lower limit of the signal-to-noise ratio coverage to The upper limit is set to ;

[0121] Set the signal-to-noise ratio resolution based on the coverage area.

[0122] In this embodiment, the signal-to-noise ratio (SNR) is divided into several segments. Based on the SNR coverage of each segment and the MCS prediction model, an SNR-MCS dynamic mapping table is generated, including:

[0123] The signal-to-noise ratio (SNR) is divided into several segments based on its coverage and resolution.

[0124] Input the lower bound of the signal-to-noise ratio coverage of each segment into the MCS prediction model to obtain the corresponding MCS prediction value;

[0125] By constructing a mapping between the coverage area of ​​the signal-to-noise ratio and the corresponding MCS prediction value, an SNR-MCS dynamic mapping table is generated.

[0126] Step 3.8: Use the SNR-MCS dynamic mapping table for interaction between users and base stations, and collect signal-to-noise ratio, modulation and coding scheme, and transmission feedback to update the data pool.

[0127] Step 4: Repeat Step 3 to retrain the probabilistic prediction model using the updated data pool, achieving dynamic optimization and iterative updating of the SNR-MCS mapping table. The data pool update mechanism is as follows: Figure 2 As shown.

[0128] Figure 4 This is a simulation diagram comparing the spectral efficiency performance of the present invention and the outer loop link adaptive algorithm under different signal-to-noise ratios, provided by an embodiment of the present invention. Figure 5 This is a simulation diagram comparing the bit error rate performance of the present invention and the outer loop link adaptive algorithm under different signal-to-noise ratios, provided by an embodiment of the present invention, wherein the parameter settings are as follows:

[0129] In this embodiment, the target communication system has 32 antennas at both the transmitting and receiving ends, uses the Rayleigh channel model, and has 2 paths. LDPC coding is used for the channel, and the decoding method is Belief Propagation (BP) algorithm with a code length of 1024. The data pool size is 100.

[0130] In this embodiment, the learning rate of the probabilistic prediction model is set to 0.005, and the retraining cycle is 20.

[0131] In this embodiment, the modulation order and mode for initializing the MCS candidate set include QPSK, 16QAM, and 64QAM; the upper limit of the code rate is 0.9, the lower limit is 0.1, the resolution is 0.05, and the number of MCS candidate set entries is set to 48.

[0132] In this embodiment, the lower limit of SNR covered by the SNR-MCS dynamic mapping table is set to 0dB, the upper limit is set to 30dB, and the resolution is 2dB.

[0133] Example 3

[0134] Based on the same inventive concept as other embodiments, this embodiment describes a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the methods of Embodiment 1 or 2 described above.

[0135] Example 4

[0136] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including computer instructions that, when executed by a processor, implement the steps of the methods described in Embodiment 1 or 2 above.

[0137] In summary, this invention can capture channel quality changes in real time and intelligently select the optimal MCS scheme based on a trained probability prediction model. This enables the target communication system to dynamically select the modulation and coding scheme with the highest matching degree with the current SNR while ensuring transmission reliability. Compared with the traditional static mapping table scheme, this invention can improve the spectral efficiency in scenarios with rapidly changing channel conditions. Furthermore, this invention ensures that the target communication system always operates at the optimal balance point between spectral efficiency and transmission reliability through a continuously updated SNR-MCS dynamic mapping table. This invention solves the problem of low transmission efficiency and reliability caused by the inability of existing technologies to adapt to constantly changing channel conditions.

[0138] This invention continuously updates the SNR-MCS mapping relationship through a closed-loop feedback mechanism, enabling the target communication system to automatically select the optimal modulation and coding scheme based on real-time channel conditions. This maximizes spectrum utilization efficiency while ensuring transmission reliability. By using a screening strategy that maximizes the product of spectrum efficiency and transmission success rate, this invention breaks through the efficiency bottleneck of traditional MCS selection schemes and achieves better spectrum resource allocation under the same bit error rate.

[0139] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0141] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0143] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for designing an SNR-MCS dynamic mapping table, characterized in that, include: The system acquires communication data between the user and the base station and constructs a data pool, wherein the communication data includes signal-to-noise ratio, modulation and coding scheme, and transmission feedback. Train a probabilistic prediction model using a data pool; Repeat the following steps to continuously update the SNR-MCS dynamic mapping table: Based on the signal-to-noise ratio and modulation and coding scheme, a real-time prediction is made using a trained probability prediction model, and the probability of successful transmission of the communication data is output. Establish an MCS candidate set based on the modulation and coding schemes in the data pool; Based on the success probability of the communication data transmission, a candidate set of MCS models is selected to construct an MCS prediction model; The signal-to-noise ratio in the actual operating environment is collected and input into the MCS prediction model, and the corresponding modulation and coding scheme is output. The user and base station interact using the corresponding modulation and coding scheme, and the transmission feedback is collected. The data pool is updated based on the signal-to-noise ratio, the corresponding modulation and coding scheme, and the transmission feedback. The signal-to-noise ratio (SNR) is divided into several segments based on the coverage and resolution of the SNR in the data pool. Based on the coverage of the SNR in each segment and the MCS prediction model, an SNR-MCS dynamic mapping table is generated. The SNR-MCS dynamic mapping table is used for interaction between users and base stations, and the signal-to-noise ratio, modulation and coding scheme, and transmission feedback are collected to update the data pool. The probabilistic prediction model is retrained using the updated data pool.

2. The SNR-MCS dynamic mapping table design method according to claim 1, characterized in that, The probability prediction model includes a binary classification neural network with two hidden layers and a Hard-Sigmoig function. After the signal-to-noise ratio and modulation coding scheme are processed by the binary classification neural network, the data is input into the Hard-Sigmoig function for further processing, and the output is the probability of successful transmission.

3. The SNR-MCS dynamic mapping table design method according to claim 2, characterized in that, The probability of successful transmission is expressed as: ； In the formula, This represents the signal-to-noise ratio based on the interaction between the user and the base station. and modulation coding method The obtained transmission success probability, where, This indicates that the transmission was successful. This represents the Hard-Sigmoig function. This represents the signal-to-noise ratio (SNR) of the interaction between the user and the base station received by the binary classification neural network. and modulation coding method The output of .

4. The SNR-MCS dynamic mapping table design method according to claim 2, characterized in that, The loss function for training the probabilistic prediction model is expressed as: ； In the formula, This represents the loss value of the weights in a binary classification neural network. This represents the number of training data samples in the data pool. This represents the first training data in the data pool. One sample, Indicates the first One sample was successfully transmitted. Represents the logarithmic function. This indicates the first interaction between the user and the base station. Signal-to-noise ratio of each sample and modulation coding method The obtained transmission success probability.

5. The SNR-MCS dynamic mapping table design method according to claim 1, characterized in that, A candidate set of MCSs is established based on the modulation and coding schemes in the data pool, including: The modulation order setting range of the modulation coding scheme is determined based on the minimum and maximum modulation order of the MCS in the data pool; The bit rate setting range of the modulation and coding scheme is determined based on the minimum and maximum bit rates of the MCS in the data pool; Multiple modulation order values ​​are selected within the modulation order setting range, and multiple code rate values ​​are selected within the code rate setting range. The selected modulation order values ​​and the selected code rate values ​​are cross-combined to generate candidate MCS entries. Traverse the candidate MCS entries: If the spectral efficiency of one candidate MCS entry is higher than that of another candidate MCS entry, and the modulation order is lower than that of another candidate MCS entry, then the other candidate MCS entry is eliminated. After the traversal is complete, we obtain the candidate set of MCS.

6. The SNR-MCS dynamic mapping table design method according to claim 5, characterized in that, The MCS candidate set includes several MCS entries, each of which includes an index identifier, modulation scheme or order, code rate, and spectral efficiency. The index identifier and spectral efficiency have a monotonically increasing relationship.

7. The SNR-MCS dynamic mapping table design method according to claim 6, characterized in that, Based on the success probability of the communication data transmission, a candidate set of MCSs is selected, and an MCS prediction model is constructed, including: Traverse the MCS candidate set; For each MCS entry, calculate the product of spectral efficiency and transmission success probability; The MCS entry that maximizes the product of spectral efficiency and transmission success probability is selected as the MCS prediction model.

8. The SNR-MCS dynamic mapping table design method according to claim 7, characterized in that, The MCS entry representing the maximum product of spectral efficiency and transmission success probability is: ； In the formula, The MCS entry represents the maximum value of the product of spectral efficiency and transmission success probability, where... Indicates the signal-to-noise ratio. Indicates to make The MCS entry that yields the maximum value. This represents the signal-to-noise ratio based on the interaction between the user and the base station. and modulation coding method The obtained transmission success probability Modulation coding method Spectral efficiency.

9. The SNR-MCS dynamic mapping table design method according to claim 1, characterized in that, The method for determining the signal-to-noise ratio coverage and resolution in the data pool includes: The mean of the signal-to-noise ratio is calculated by statistically analyzing the signal-to-noise ratio in the data pool. and standard deviation ; Set the lower limit of the signal-to-noise ratio coverage to The upper limit is set to ; Set the signal-to-noise ratio resolution based on the coverage area of ​​the signal-to-noise ratio.

10. The SNR-MCS dynamic mapping table design method according to claim 9, characterized in that, The signal-to-noise ratio (SNR) is divided into several segments. Based on the SNR coverage of each segment and the MCS prediction model, a dynamic SNR-MCS mapping table is generated, including: The signal-to-noise ratio (SNR) is divided into several segments based on its coverage and resolution. Input the lower bound of the signal-to-noise ratio coverage of each segment into the MCS prediction model to obtain the corresponding MCS prediction value; By constructing a mapping between the coverage area of ​​the signal-to-noise ratio and the corresponding MCS prediction value, an SNR-MCS dynamic mapping table is generated.

Images